1.1 Slice Submission

This is the detailed slice information we got from collaborators:

1.2 Sample Information

A brief sample information is generated from the submission table for the following analysis.

“SD” refers to “Sleep Deprivation” while “nSD” refers to “No Sleep Deprivation”.

1.3 MERSCOPE Data Quality Summary

The summaries present the data quality assessment automatically generated by MERSCOPE for each experiment. We mainly focus on the transcripts level for each sample. So we’re looking for high density in transcripts, based on the transcripts count per field of view (FOV), transcript density in FOV, and frequency of transcripts detected.

Need to note that the low accuracy in DAPI cell boundary is not a concern, as a self-designed cell segmentation processing will take over this task.

MERSCOPE Data Quality Summary

MERSCOPE Data Quality Summary

MERSCOPE Data Quality Summary

MERSCOPE Data Quality Summary

MERSCOPE Data Quality Summary

MERSCOPE Data Quality Summary

MERSCOPE Data Quality Summary

MERSCOPE Data Quality Summary

MERSCOPE Data Quality Summary

MERSCOPE Data Quality Summary

MERSCOPE Data Quality Summary

MERSCOPE Data Quality Summary

2.1 Cell Segmentation

Based on the spatial information and images obtained from MERFISH, we developed a machine learning model using the Cellpose algorithm to distinguish individual cells via MERFISH DAPI images.

To ensure the data quality and accuracy of cells, we have defined the minimum and maximum values for cell volume and gene count per cell. The cell volume should be between [100, 1800], and the gene count per cell > 25. After filter the outliers, the qualified cells count is shown in the following table.

Outliers were filtered from the data, and the qualified cells count is presented in the following table. The identified cells after filtering are listed in the following table. In total, the study identified 716157 cells across 12 samples.

Identified Cell Spatial Overview on all Samples

2.2 Batch Effect & Dimension Reduction

We use Scanpy for the analysis of single-cell level transcriptome data. The initial stage of our analysis involves the elimination of batch effects, thereby ensuring that different samples from various batches are distributed within the same domain and are statistically reasonable to be integrated and compared. To achieve this, we utilize the Harmony algorithm.

Subsequently, we present visualizations of the batch difference by Leiden UMAP clusters. Also, we illustrate the distributions of the Leiden clusters for future analysis.

Umap of cells and colored by batch

Umap of cells on each batch

Leiden Umap can be dividied to 28 clusters

2.3 Cell Annotation

We use a recent published tool: Map My Cell to perform cell type annotations for each cell. It is a high resolution cell type annotator build by Alan Institude, with nested levels of classification including 34 classes and 338 subclasses.

The taxonomy is based on the Allen Mouse Brain Common Coordinate Framework version 3 (CCFv3)[https://doi.org/10.1016/j.cell.2020.04.007]. Part of the used abbreviations is list in the supplementary Abbreviation. Otherwise can be found in CCFv3 paper.

With the annotation, we can identify and plot the selected types of cells in each sample.

2.3.1 Spatial cell type (Neurons)

2.3.2 Spatial cell type (Non-Neurons)

3.1 Adrenergic Related Gene

Before gene differentiation. We can start with searching potential target genes. Via NCBI Gene Database, we search for adrenergic related genes and subsequently compared them with the MERFISH gene panel. This resulted in the detection of 43 overlapping genes, which we consider as adrenergic related and will be highlighted in gene differentiation analyses.

3.2 18 month (SD v.s. nSD)

Here, we use Wilcoxon rank-sum to compute differential expression. The statistical significance was cut-off by log2(FoldChange) > 1 and p_adj < 0.05.

3.3 4 month (SD v.s. nSD)

Here, we use Wilcoxon rank-sum to compute differential expression. The statistical significance was cut-off by log2(FoldChange) > 1 and p_adj < 0.05.

3.4.1 18 month, HY (Hypothalamus) Gaba, SD v.s. nSD

Here, we use Wilcoxon rank-sum to compute differential expression. The statistical significance was cut-off by log2(FoldChange) > 1 and p_adj < 0.05.

3.4.2 4 month, HY (Hypothalamus) Gaba, SD v.s. nSD

3.4.3 18 month, PIR (piriform cortex) Glut, SD v.s. nSD

3.4.4 4 month, PIR (piriform cortex) Glut, SD v.s. nSD

3.4.5 18 month, Endothelial Cell, SD v.s. nSD

3.4.6 4 month, Endothelial Cell, SD v.s. nSD

Cell types & Regions

Astro, Astrocyte;

ABC, arachnoid barrier cells;

BAM, border-associated macrophages;

BLA, Basolateral amygdala;

CB, cerebellum;

CGE, caudal ganglionic eminence;

CHOR, choroid plexus;

CNU, cerebral nuclei;

CR, Cajal–Retzius;

CT, corticothalamic;

CTX, cerebral cortex;

CTXsp, cortical subplate;

DC, dendritic cells;

DCO, dorsal cochlear nucleus;

DG, dentate gyrus;

EA, extended amygdala;

Endo, endothelial cells;

ENT, Entorhinal area;

ENTl, Entorhinal area, lateral part;

Epen, ependymal;

EPI, epithalamus;

ET, extratelencephalic;

GC, granule cell;

HB, hindbrain;

HPF, hippocampal formation;

HY, hypothalamus;

HYa, anterior hypothalamic;

IMN, immature neurons;

IT, intratelencephalic;

L6b, layer 6b;

LGE, lateral ganglionic eminence;

LH, lateral habenula;

LSX, lateral septal complex;

MB, midbrain;

MGE, medial ganglionic eminence;

MH, medial habenula;

MM, medial mammillary nucleus;

MY, medulla;

NN, non-neuronal;

NP, near-projecting;

NT, non-telencephalon;

OB, olfactory bulb;

OEC, olfactory ensheathing cells;

OLF, olfactory areas;

Oligo, oligodendrocytes;

OPC, oligodendrocyte precursor cells;

P, pons;

PAL, pallidum;

Peri, pericytes;

PIR, piriform cortex;

SMC, smooth muscle cells;

STR, striatum;

TE, telencephalon;

TH, thalamus;

UBC, unipolar brush cells;

VLMC, vascular leptomeningeal cells.

Neurotransmitter types

Chol, cholinergic;

Dopa, dopaminergic;

GABA, GABAergic;

Glut, glutamatergic;

Glyc, glycinergic;

Hist, histaminergic;

Nora, noradrenergic;

Sero, serotonergic